{
  "id": "oai:papers.qnfo.org:kernel-as-a-verifiable-prompt-specification-framework-six-properties-composition",
  "slug": "kernel-as-a-verifiable-prompt-specification-framework-six-properties-composition",
  "title": "KERNEL as a Verifiable Prompt Specification Framework: Six Properties, Composition Bounds, and a Benchmark Design",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Prompt engineering for large language models (LLMs) is practiced as a craft of heuristics, yet teams deploying LLM agents need prompts whose behavior can be specified, checked, and reasoned about the way safety-critical software is specified. The gap is formal: no existing framework states prompt quality as a set of machine-checkable constraints on a prompt specification, and no analysis quantifies what such constraints would buy in first-try success, revision cost, or run-to-run consistency. We formalize the KERNEL properties—simplicity, verifiability, reproducibility, narrow scope, explicit constraints, and structured format—as six binary predicates on a prompt specification, prove a composition result that chained narrow, verifiable sub-prompts dominate monolithic prompts under stated independence assumptions, and derive the benchmark design that would test the claim. The principal computed results: a monolithic prompt with five independent requirements each met with probability $0.8$ succeeds with probability $0.32768$, while the same workload decomposed into five verified sub-prompts with one bounded repair each succeeds with probability $0.59049$, a $1.80$-fold improvement, at a projected $2.20$-fold reduction in input-token cost per successful completion; a one-at-a-time ablation benchmark over six properties, ten tasks, and three models requires 1,800 runs. The framework turns prompt design from phrasing advice into specification and conformance checking, with the benchmark as the immediate next test.",
  "abstract_markdown": "Prompt engineering for large language models (LLMs) is practiced as a craft of heuristics, yet teams deploying LLM agents need prompts whose behavior can be specified, checked, and reasoned about the way safety-critical software is specified. The gap is formal: no existing framework states prompt quality as a set of machine-checkable constraints on a prompt specification, and no analysis quantifies what such constraints would buy in first-try success, revision cost, or run-to-run consistency. We formalize the KERNEL properties—simplicity, verifiability, reproducibility, narrow scope, explicit constraints, and structured format—as six binary predicates on a prompt specification, prove a composition result that chained narrow, verifiable sub-prompts dominate monolithic prompts under stated independence assumptions, and derive the benchmark design that would test the claim. The principal computed results: a monolithic prompt with five independent requirements each met with probability $0.8$ succeeds with probability $0.32768$, while the same workload decomposed into five verified sub-prompts with one bounded repair each succeeds with probability $0.59049$, a $1.80$-fold improvement, at a projected $2.20$-fold reduction in input-token cost per successful completion; a one-at-a-time ablation benchmark over six properties, ten tasks, and three models requires $1{,}800$ runs. The framework turns prompt design from phrasing advice into specification and conformance checking, with the benchmark as the immediate next test.",
  "date": "2026-11-12",
  "first_published": "2026-11-12",
  "last_revised": "2026-11-12T00:00:00Z",
  "modified": "2026-11-12T00:00:00Z",
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  "peer_review_status": "Not independently peer-reviewed",
  "resource_type": "ScholarlyArticle",
  "doi": null,
  "doi_url": null,
  "url": "https://papers.qnfo.org/papers/kernel-as-a-verifiable-prompt-specification-framework-six-properties-composition",
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  "sha256": "ac02f8be544b87b9b93c3407664d7d09a2214266667ee442838953cbbdca9264",
  "version_url": "https://papers.qnfo.org/papers/kernel-as-a-verifiable-prompt-specification-framework-six-properties-composition/v/1.0.0",
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        "name": "Rowan Brad Quni-Gudzinas",
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    "titles": [
      {
        "title": "KERNEL as a Verifiable Prompt Specification Framework: Six Properties, Composition Bounds, and a Benchmark Design"
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    "publicationYear": "2026",
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        "description": "Prompt engineering for large language models (LLMs) is practiced as a craft of heuristics, yet teams deploying LLM agents need prompts whose behavior can be specified, checked, and reasoned about the way safety-critical software is specified. The gap is formal: no existing framework states prompt quality as a set of machine-checkable constraints on a prompt specification, and no analysis quantifies what such constraints would buy in first-try success, revision cost, or run-to-run consistency. We formalize the KERNEL properties—simplicity, verifiability, reproducibility, narrow scope, explicit constraints, and structured format—as six binary predicates on a prompt specification, prove a composition result that chained narrow, verifiable sub-prompts dominate monolithic prompts under stated independence assumptions, and derive the benchmark design that would test the claim. The principal computed results: a monolithic prompt with five independent requirements each met with probability $0.8$ succeeds with probability $0.32768$, while the same workload decomposed into five verified sub-prompts with one bounded repair each succeeds with probability $0.59049$, a $1.80$-fold improvement, at a projected $2.20$-fold reduction in input-token cost per successful completion; a one-at-a-time ablation benchmark over six properties, ten tasks, and three models requires 1,800 runs. The framework turns prompt design from phrasing advice into specification and conformance checking, with the benchmark as the immediate next test.",
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